Multi-Mineral Segmentation of SEM Images Using Deep Learning Techniques

2021 ◽  
Author(s):  
Vladislav Vasilevich Alekseev ◽  
Denis Mihaylovich Orlov ◽  
Dmitry Anatolevich Koroteev
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Pore Space ◽  
Rock Physics ◽  
Sem Images ◽  
Digital Rock Physics ◽  
Learning Techniques ◽  
Digital Core ◽  
Non Destructive

Abstract The approaches of building and methods of using the digital core are currently developing rapidly. The use of these methods makes it possible to obtain petrophysical information by non-destructive methods quickly. Digital rock physics includes two main stages: constructing models and modeling various physical processes on the obtained models. Our work proposes using deep learning methods for mineral and pore space segmentation instead of classical methods such as threshold image processing. Deep neural networks have long been able to show their advantages in many areas of computer vision. This paper proposes and tests methods that help identify different minerals in images from a scanning electron microscope. We used images of rocks of the Achimov formation, which are arkoses, as samples. We tested various deep neural networks such as LinkNet, U-Net, ResUNet, and pix2pix and identified those that performed best in segmentation.

scholarly journals Bike Sharing Prediction using Deep Neural Networks

2017 ◽  
Vol 1 (3) ◽  
pp. 83 ◽  
Author(s):  
Chandrasegar Thirumalai ◽  
Ravisankar Koppuravuri
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Neural Nets ◽  
Machine Learning Techniques ◽  
Business Decisions ◽  
Learning Techniques ◽  
Bike Sharing ◽  
The Way

In this paper, we will use deep neural networks for predicting the bike sharing usage based on previous years usage data. We will use because deep neural nets for getting higher accuracy. Deep neural nets are quite different from other machine learning techniques; here we can add many numbers of hidden layers to improve the accuracy of our prediction and the model can be trained in the way we want such that we can achieve the results we want. Nowadays many AI experts will say that deep learning is the best AI technique available now and we can achieve some unbelievable results using this technique. Now we will use that technique to predict bike sharing usage of a rental company to make sure they can take good business decisions based on previous years data.

Bearing fault diagnosis using deep learning techniques coupled with handcrafted feature extraction: A comparative study

2020 ◽  
pp. 107754632092914
Author(s):  
Mohammed Alabsi ◽  
Yabin Liao ◽  
Ala-Addin Nabulsi
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Deep Learning ◽  
Comparative Study ◽  
Domain Knowledge ◽  
Deep Neural Networks ◽  
Performance Limits ◽  
Data Repositories ◽  
Learning Techniques ◽  
Wide Range

Deep learning has seen tremendous growth over the past decade. It has set new performance limits for a wide range of applications, including computer vision, speech recognition, and machinery health monitoring. With the abundance of instrumentation data and the availability of high computational power, deep learning continues to prove itself as an efficient tool for the extraction of micropatterns from machinery big data repositories. This study presents a comparative study for feature extraction capabilities using stacked autoencoders considering the use of expert domain knowledge. Case Western Reserve University bearing dataset was used for the study, and a classifier was trained and tested to extract and visualize features from 12 different failure classes. Based on the raw data preprocessing, four different deep neural network structures were studied. Results indicated that integrating domain knowledge with deep learning techniques improved feature extraction capabilities and reduced the deep neural networks size and computational requirements without the need for exhaustive deep neural networks architecture tuning and modification.

scholarly journals An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning

2021 ◽  
Vol 11 (18) ◽  
pp. 8441
Author(s):  
Anh-Cang Phan ◽  
Ngoc-Hoang-Quyen Nguyen  ◽  
Thanh-Ngoan Trieu ◽  
Thuong-Cang Phan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Training Dataset ◽  
Economic Losses ◽  
Drowsy Driving ◽  
Training Time ◽  
Road Users ◽  
Fast Training ◽  
Learning Techniques

Drowsy driving is one of the common causes of road accidents resulting in injuries, even death, and significant economic losses to drivers, road users, families, and society. There have been many studies carried out in an attempt to detect drowsiness for alert systems. However, a majority of the studies focused on determining eyelid and mouth movements, which have revealed many limitations for drowsiness detection. Besides, physiological measures-based studies may not be feasible in practice because the measuring devices are often not available on vehicles and often uncomfortable for drivers. In this research, we therefore propose two efficient methods with three scenarios for doze alert systems. The former applies facial landmarks to detect blinks and yawns based on appropriate thresholds for each driver. The latter uses deep learning techniques with two adaptive deep neural networks based on MobileNet-V2 and ResNet-50V2. The second method analyzes the videos and detects driver’s activities in every frame to learn all features automatically. We leverage the advantage of the transfer learning technique to train the proposed networks on our training dataset. This solves the problem of limited training datasets, provides fast training time, and keeps the advantage of the deep neural networks. Experiments were conducted to test the effectiveness of our methods compared with other methods. Empirical results demonstrate that the proposed method using deep learning techniques can achieve a high accuracy of 97% . This study provides meaningful solutions in practice to prevent unfortunate automobile accidents caused by drowsiness.

scholarly journals Deep Learning for the classification of quenched jets

2021 ◽  
Vol 2021 (11) ◽  
Author(s):  
L. Apolinário ◽  
N. F. Castro ◽  
M. Crispim Romão ◽  
J. G. Milhano ◽  
R. Pedro ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Quark Gluon Plasma ◽  
Recurrent Neural Networks ◽  
Heavy Ions ◽  
Deep Neural Networks ◽  
Gluon Plasma ◽  
Jet Quenching ◽  
Learning Techniques

Abstract An important aspect of the study of Quark-Gluon Plasma (QGP) in ultrarelativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP. In this work, we propose studying Deep Learning techniques for this purpose. Samples of Z+jet events were simulated in vacuum (pp collisions) and medium (PbPb collisions) and used to train Deep Neural Networks with the objective of discriminating between medium- and vacuum-like jets within the medium (PbPb) sample. Dedicated Convolutional Neural Networks, Dense Neural Networks and Recurrent Neural Networks were developed and trained, and their performance was studied. Our results show the potential of these techniques for the identification of jet quenching effects induced by the presence of the QGP.

scholarly journals Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective

2021 ◽  
Vol 12 ◽  
Author(s):  
Patrick Thiam ◽  
Heinke Hihn ◽  
Daniel A. Braun ◽  
Hans A. Kestler ◽  
Friedhelm Schwenker
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Pain Intensity ◽  
Supervised Learning ◽  
Pain Assessment ◽  
Deep Neural Networks ◽  
Fine Tuning ◽  
Physiological Data ◽  
Intensity Assessment ◽  
Learning Techniques

Traditional pain assessment approaches ranging from self-reporting methods, to observational scales, rely on the ability of an individual to accurately assess and successfully report observed or experienced pain episodes. Automatic pain assessment tools are therefore more than desirable in cases where this specific ability is negatively affected by various psycho-physiological dispositions, as well as distinct physical traits such as in the case of professional athletes, who usually have a higher pain tolerance as regular individuals. Hence, several approaches have been proposed during the past decades for the implementation of an autonomous and effective pain assessment system. These approaches range from more conventional supervised and semi-supervised learning techniques applied on a set of carefully hand-designed feature representations, to deep neural networks applied on preprocessed signals. Some of the most prominent advantages of deep neural networks are the ability to automatically learn relevant features, as well as the inherent adaptability of trained deep neural networks to related inference tasks. Yet, some significant drawbacks such as requiring large amounts of data to train deep models and over-fitting remain. Both of these problems are especially relevant in pain intensity assessment, where labeled data is scarce and generalization is of utmost importance. In the following work we address these shortcomings by introducing several novel multi-modal deep learning approaches (characterized by specific supervised, as well as self-supervised learning techniques) for the assessment of pain intensity based on measurable bio-physiological data. While the proposed supervised deep learning approach is able to attain state-of-the-art inference performances, our self-supervised approach is able to significantly improve the data efficiency of the proposed architecture by automatically generating physiological data and simultaneously performing a fine-tuning of the architecture, which has been previously trained on a significantly smaller amount of data.

scholarly journals Combining content and social features in a deep learning approach to Vietnamese email prioritization

2021 ◽  
Author(s):  
Ha Thanh Nguyen ◽  
Quan Dinh Dang ◽  
Anh Quang Tran
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
New Method ◽  
Learning Approach ◽  
Field Of Study ◽  
Learning Techniques ◽  
Email Overload ◽  
Social Features

The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work.

scholarly journals Phishing Website Detection using Supervised Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 4844-4846
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Personal Information ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Web Pages ◽  
Web Page ◽  
Algorithmic Approach ◽  
Learning Techniques

The website phishing is the tremendously growing problem over the internet which will lead to the loss of personal information. This process will run like, when ever user clicks a website link it will lead them to the web page that is created by the phisher to deceive the user. After this phishing has been started in order to stop it many techniques came into existence to detect the phished web site and help the user from being deceived by the attacker. Even though many techniques have adapted to stop the attackers, it is difficult because as many phished web pages are generated by the attackers within few hours. Most of the techniques to detect these phishing websites are not able to decide the fake website with legitimate one because the accuracy of getting results are much less. There are many supervised machine learning techniques which are supervised, where a primary set of data is given to the algorithm and depending on that set the algorithm will be trained and it will predict the results for the same. One of the most important techniques that is deep Learning classifiers is applied with significant features to detect phishing websites. By using this algorithm we can classify the phishing websites from genuine websites by using effective features. In this algorithmic approach to detect genuine websites a feature set is used so by analyzing these features using deep neural networks we can detect a website is phished or not. We can also increase the accuracy of our algorithm by adding certain more features and increasing the hidden layers in neural networks.

scholarly journals Neural Networks for Entity Matching: A Survey

2021 ◽  
Vol 15 (3) ◽  
pp. 1-37
Author(s):  
Nils Barlaug ◽  
Jon Atle Gulla
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Challenging Problem ◽  
Entity Matching ◽  
New Methods ◽  
Matching Process ◽  
Learning Techniques

Entity matching is the problem of identifying which records refer to the same real-world entity. It has been actively researched for decades, and a variety of different approaches have been developed. Even today, it remains a challenging problem, and there is still generous room for improvement. In recent years, we have seen new methods based upon deep learning techniques for natural language processing emerge. In this survey, we present how neural networks have been used for entity matching. Specifically, we identify which steps of the entity matching process existing work have targeted using neural networks, and provide an overview of the different techniques used at each step. We also discuss contributions from deep learning in entity matching compared to traditional methods, and propose a taxonomy of deep neural networks for entity matching.

scholarly journals Detection and localization of cotton based on deep neural networks

2021 ◽  
Vol 9 (4) ◽  
pp. 17-22
Author(s):  
Annapoorna B R ◽  
Dr. Ramesh Babu D R
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Data Set ◽  
Detection Algorithms ◽  
Learning Techniques ◽  
Detection Of Objects ◽  
Detection And Localization

Cotton detection is the localization and identification of the cotton in an image. It has a wide application in robot harvesting.  Various modern algorithms use deep learning techniques for detection of fruits/flowers. As per the survey, the topics travelled include numerous algorithms used, and accuracy obtained on using those algorithms on their data set. The limitations and the advantages in each paper, are also discussed. This paper focuses on various fruit detection algorithms- the Faster RCNN, the RCNN, YOLO. Ultimately, a rigorous survey of many papers related to the detection of objects like fruits/flowers, analysis of the assets and faintness of each paper leads us to understanding the techniques and purpose of algorithms.  

scholarly journals Survey on Object Tracking using Deep Learning Paradigms

2020 ◽  
Vol 8 (4) ◽  
pp. 34-46
Author(s):  
Saad Alhuzami
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Tracking ◽  
Deep Neural Networks ◽  
Final Section ◽  
Comprehensive Overview ◽  
Convolutional Networks ◽  
Detection And Tracking ◽  
Continuous Sequence ◽  
Learning Techniques

The field of object tracking extends across different domains. It is a major key player in the field of image processing and pattern recognition. Object tracking is the process of tracking an object over a continuous sequence of image frames to determine over time the relative movements or changes.  With the massive advancements in the field of deep learning, the use of deep neural networks has risen due to their impressive accomplishments in object detection and tracking. In this Survey, the objective is to give a comprehensive overview of the recent attempts in the field of object tracking with a focus on the use of deep learning techniques and algorithms. The paper is divided into four sections; at first, we will give an overview of the recent work to highlight the techniques and methods which have been used in object tracking using deep learning. The second section focus is on the object tracking that uses convolutional networks techniques. The third section focuses on some of the recurrent neural networks to tack objects. The final section is concentrated on auto-encoders object tracking.

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